Markov random field modeling in image analysis
Markov random field modeling in image analysis
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facts, Conjectures, and Improvements for Simulated Annealing
Facts, Conjectures, and Improvements for Simulated Annealing
Range Image Segmentation by an Effective Jump-Diffusion Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Point Processes for Unsupervised Line Network Extraction in Remote Sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum
Journal of Mathematical Imaging and Vision
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Marked point process for vascular tree extraction on angiogram
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Geometric Feature Extraction by a Multimarked Point Process
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial statistics of visual keypoints for texture recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
A marked point process for modeling lidar waveforms
IEEE Transactions on Image Processing
A 3-D marked point process model for multi-view people detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation
International Journal of Computer Vision
Efficient monte carlo sampler for detecting parametric objects in large scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Recovering Line-Networks in Images by Junction-Point Processes
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
| Hi-index | 0.00 |
Point processes constitute a natural extension of Markov random fields (MRF), designed to handle parametric objects. They have shown efficiency and competitiveness for tackling object extraction problems in vision. Simulating these stochastic models is however a difficult task. The performances of the existing samplers are limited in terms of computation time and convergence stability, especially on large scenes. We propose a new sampling procedure based on a Monte Carlo formalism. Our algorithm exploits the Markovian property of point processes to perform the sampling in parallel. This procedure is embedded into a data-driven mechanism so that the points are distributed in the scene in function of spatial information extracted from the input data. The performances of the sampler are analyzed through a set of experiments on various object detection problems from large scenes, including comparisons to the existing algorithms. The sampler is also tested as optimization algorithm for MRF-based labeling problems.